The face is a distinct, if not unique, identification of a person. Face recognition is a natural ability of human beings. It was reported by S. J. Thorpe in "Traitement d'images par la systeme visuel de I'homme" (Actes du Colloque TIPI 8 8, pp-LX-I-IX-12, Aussois, Avril 1988) that there exists a specific cortex area in the human brain to perform this task. As a personal identifier, among fingerprint, hand geometry and retinal pattern, the facial identifier has the highest social acceptability. Therefore, the automatic recognition of human faces is emerging as an active research area. Two good surveys are contained in the work of A. Samal and P. Iyengar, "Automatic recognition and analysis of human faces and facial expressions: A survey" (Patt. Recog., vol. 25, pp-65-77, 1992) and R. Chellapa, C. L. Wilson and S. Sirohey, "Human and machine recognition of faces: A survey" (Proceeding of IEEE, vol. 83, pp.705-740, 1995). These surveys outline a current state of the art.
Most of the existing methods need to extract features, such as structural features, transform coefficient features, templated features. Based on these features, the recognition is then performed basically by means of one of two known approaches: statistical and neural network. Though these methods have shown considerable promise, they both suffer from several disadvantages. In both cases, significant training is required. Both approaches are somewhat lighting sensitive and much of the training is to account for lighting differences between stores features and those captured for recognition. Thus far, due to complexity and reliability, systems employing these approaches have not received widespread acceptance.
In face recognition applications, range imaging provides significant advantages since it contains richer and more accurate shape information than that captured in two-dimensional intensity images. An early approach to identifying facial images using range images compares two profile planes obtained from two separate range images of faces; this was reported by [25] T. Y. Cartoux, J. T. Lapreste and M. Richentin in "Face authentification or recognition by profile extraction from range images" (Proc.: IEEE, Computer Soc. Workshop on Interpretation of 3D Scenes, 1989, Austin, Tex., pp. 194-199). Unfortunately, such a compression is based on only a small portion of facial information.
J. C. Lee and E. Milios disclose an approach using convex regions of a range face in an article "Matching range images of human faces" (Proc. IEEE Soc. 3d Int. Conf. On Computer Vision, 1990, pp.722-726). The regions are segmented and then represented by an extended Gaussian image, which is invariant to scale and orientation changes. To identify two faces, a matching algorithm is used. The algorithm appears to work as intended. Unfortunately, for most facial recognition operations, scale independence is of little value. Changes in orientation are often minimal since a face is most often upright and images are usually captured under controlled conditions.
T. Abe, H. Aso and M. Kimara in "Automatic Identification of human faces by 3-D shape of Surface--using vertices of B-Spline surface" (System and Computers in Japan, vol. 22, No.7, pp.96-105, 1991) disclose a method wherein vortices of a B-Spline surface are used as feature vector components for range face identification. Gordin in "Face recognition based on depth maps and surface curvature" (SPIE Proc.: Geometric Methods in Computer Vision, vol. 1570, 1991) describes a curvature based recognition system. A range image is obtained from a rotating scanner and stored in a cylindrical coordinate system. Surface descriptors are calculated from local curvature values, and comparisons between the descriptors of two faces is used for the identification. Systems of this type are useful when a plurality of range images of individuals are already accessible. Currently, most databases of facial images comprise two-dimensional face images.
It would be advantageous to provide a method of comparing two-dimensional facial images and three-dimensional facial images.
A major problem in comparing range images and two-dimensional intensity images is illumination. Two two-dimensional intensity images may appear very differently when illumination is different. Shadows act to obscure features and to limit recognition. Vastly different images result by modifying illumination. Conversely, range images have no illumination component and, therefore, are unaffected by illumination. That said, comparing a range image and a two-dimensional intensity image by rendering a two-dimensional image with illumination in order to match illumination of the intensity image is impracticable as a large number of images are rendered before the illumination substantially matches and a comparison is possible.
Thus, in an attempt to overcome these and other limitations of known prior art devices, it is an object of this invention to provide a method of performing facial recognition that is substantially independent of illumination.